Death is the new sex, last great taboo in Western society and Western medicine, as Richard Smith discusses in his editorial (p 129). In the trinity of births, marriages, and deaths, only death does not have glossy magazines devoted to stylish consumption at the attendant ceremonies. On the web, of course, …

Death and dying

Code clone detection is an important problem for software maintenance and evolution. Many approaches consider either structure or identifiers, but none of the existing detection techniques model both sources of information. These techniques also depend on generic, handcrafted features to represent code fragments. We introduce learning-based detection techniques where everything for representing terms and fragments in source code is mined from the repository. Our code analysis supports a framework, which relies on deep learning, for automatically linking patterns mined at the lexical level with patterns mined at the syntactic level. We evaluated our novel learning-based approach for code clone detection with respect to feasibility from the point of view of software maintainers. We sampled and manually evaluated 398 file- and 480 method-level pairs across eight real-world Java systems; 93% of the file- and method-level samples were evaluated to be true positives. Among the true positives, we found pairs mapping to all four clone types. We compared our approach to a traditional structure-oriented technique and found that our learning-based approach detected clones that were either undetected or suboptimally reported by the prominent tool Deckard. Our results affirm that our learning-based approach is suitable for clone detection and a tenable technique for researchers.

Deep learning code fragments for code clone detection

Research at the intersection of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit code's abundance of patterns. In this article, we survey this work. We contrast programming languages against natural languages and discuss how these similarities and differences drive the design of probabilistic models. We present a taxonomy based on the underlying design principles of each model and use it to navigate the literature. Then, we review how researchers have adapted these models to application areas and discuss cross-cutting and application-specific challenges and opportunities.

A Survey of Machine Learning for Big Code and Naturalness

Research at the intersection of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit the abundance of patterns of code. In this article, we survey this work. We contrast programming languages against natural languages and discuss how these similarities and differences drive the design of probabilistic models. We present a taxonomy based on the underlying design principles of each model and use it to navigate the literature. Then, we review how researchers have adapted these models to application areas and discuss cross-cutting and application-specific challenges and opportunities.

https://dl.acm.org/doi/pdf/10.1145/3212695

Refactoring is widely practiced by developers, and considerable research and development effort has been invested in refactoring tools. However, little has been reported about the adoption of refactoring tools, and many assumptions about refactoring practice have little empirical support. In this paper, we examine refactoring tool usage and evaluate some of the assumptions made by other researchers. To measure tool usage, we randomly sampled code changes from four Eclipse and eight Mylyn developers and ascertained, for each refactoring, if it was performed manually or with tool support. We found that refactoring tools are seldom used: 11 percent by Eclipse developers and 9 percent by Mylyn developers. To understand refactoring practice at large, we drew from a variety of data sets spanning more than 39,000 developers, 240,000 tool-assisted refactorings, 2,500 developer hours, and 12,000 version control commits. Using these data, we cast doubt on several previously stated assumptions about how programmers refactor, while validating others. Finally, we interviewed the Eclipse and Mylyn developers to help us understand why they did not use refactoring tools and to gather ideas for future research.

How We Refactor, and How We Know It

Approximately 70% of the source code of a software system consists of identifiers. Hence, the names chosen as identifiers are of paramount importance for the readability of computer programs and therewith their comprehensibility. However, virtually every programming language allows programmers to use almost arbitrary sequences of characters as identifiers which far too often results in more or less meaningless or even misleading naming. Coding style guides somehow address this problem but are usually limited to general and hard to enforce rules like "identifiers should be self-describing". This paper renders adequate identifier naming far more precisely. A formal model, based on bijective mappings between concepts and names, provides a solid foundation for the definition of precise rules for concise and consistent naming. The enforcement of these rules is supported by a tool that incrementally builds and maintains a complete identifier dictionary while the system is being developed. The identifier dictionary explains the language used in the software system, aids in consistent naming, and supports programmers by proposing suitable names depending on the current context.

/pdf/concise-and-consistent-naming-19nryqaufm.pdf

Concise and consistent naming

Maintainability is a key quality attribute of successful software systems. However, its management in practice is still problematic. Currently, there is no comprehensive basis for assessing and improving the maintainability of software systems. Quality models have been proposed to solve this problem. Nevertheless, existing approaches do not explicitly take into account the maintenance activities, that largely determine the software maintenance effort. This paper proposes a 2-dimensional model of maintainability that explicitly associates system properties with the activities carried out during maintenance. The separation of activities and properties facilitates the identification of sound quality criteria and allows to reason about their interdependencies. This transforms the quality model into a structured and comprehensive quality knowledge base that is usable in industrial project environments. For example, review guidelines can be generated from it. The model is based on an explicit quality metamodel that supports its systematic construction and fosters preciseness as well as completeness. An industrial case study demonstrates the applicability of the model for the evaluation of the maintainability of Matlab Simulink models that are frequently used in model-based development of embedded systems.

An Activity-Based Quality Model for Maintainability

Over time, software systems suffer gradual quality decay and therefore costs can rise if organizations fail to take proactive countermeasures. Quality control is the first step to avoiding this cost trap. Continuous quality assessments help users identify quality problems early, when their removal is still inexpensive; they also aid decision making by providing an integrated view of a software system's current status. As a side effect, continuous and timely feedback helps developers and maintenance personnel improve their skills and thereby decreases the likelihood of future quality defects. To make regular quality control feasible, it must be highly automated, and assessment results must be presented in an aggregated manner to avoid overwhelming users with data. This article offers an overview of tools that aim to address these issues. The authors also discuss their own flexible, open-source toolkit, which supports the creation of dashboards for quality control.

Tool Support for Continuous Quality Control

Due to its economic impact "maintainability" is broadly accepted as an important quality attribute of software systems. But in contrast to attributes such as performance and correctness, there is no common understanding of what maintainability actually is, how it can be achieved, measured, or assessed. In fact, every software organization of significant size seems to have its own definition of maintainability. We address this problem by defining an unique two-dimensional quality model that associates maintenance activities with system properties including the capabilities of the organization. The separation of activities and properties facilitates the identification of sound quality criteria and allows to reason about their interdependencies. The resulting quality controlling process enforces these criteria through tool-supported measurements as well as manual inspections. We report on our experiences with the incremental development of the quality model and its application to large scale commercial software projects. Among the positive effects are a slowdown of decay and a significantly increased awareness for long-term quality aspects.

Demystifying maintainability

It is remarkable to think that even without any interest in finding suitable methods and concepts that would allow complex software systems to evolve and remain manageable, the ever growing open source movement has silently managed to establish highly successful evolution techniques over the last two decades. These concepts represent best practices that could be applied equally to a number of today's most crucial problems concerning the evolution of complex commercial software systems. The authors state and explain some of these principles from the perspective of experienced open source developers, and give the rationale as to why the highly dynamic "free software development process", as a whole, is entangled with constantly growing code bases and changing project sizes, and how it deals with these successfully.

/pdf/the-contribution-of-free-software-to-software-evolution-37wooz7vni.pdf

Markus Pizka

Papers

Concise and consistent naming

An Activity-Based Quality Model for Maintainability

Tool Support for Continuous Quality Control

Demystifying maintainability

The contribution of free software to software evolution